To ensure nuclear safety, timely and accurate anomaly detection is of utmost importance in
the daily condition monitoring of Nuclear Power Plants (NPPs), as any slight anomaly in a …

In this work, a multivariate time-series machine learning meta-model is developed to predict
the transient response of a typical nuclear power plant (NPP) undergoing a steam generator …

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty
quantification. A machine learning algorithm is developed to predict the peak cladding …

To expedite the decision-making process under Nuclear Power Plant (NPP) accident
conditions, at a reduced computational cost, a Machine Learning (ML) time-series meta …

In this work, a machine learning (ML) metamodel is developed for the time‐series
forecasting of a typical nuclear power plant response undergoing a loss of coolant accident …

Based on the lessons from the Fukushima accident, new strategies to mitigate the spread of
accidents in nuclear power plants have been established that consider portable equipment …

The purpose of this study is to examine the success window of a hybrid in-vessel retention
(IVR) strategy coupled with ex-vessel cooling (ERVC) under an extended Station Blackout …

Abstract Development and evaluation of seismic fragility of structures and components is
crucial in seismic probabilistic risk assessment of nuclear power plants. Simulation-based …

The Fukushima accident showed that the safety of multiple nuclear power plants (NPPs) at
the same site could be jeopardized simultaneously. Since then, many studies have focused …

During accident scenarios at nuclear power plants, which include the loss of coolant
inventory, it is essential to restore the core cooling capability. Normally, this is achieved by …